Computing

作为一个重度AI用户，我在经历长期严重失眠后没有走常规的"排除变量"路线，而是用AI写了一个iOS app导出HealthKit数据，做多变量回归分析找到了真正的原因——晚上使用AI高强度思考。这篇文章分享了AI如何在全链条上提供执行力支持，也反思了人的judgment和认知上的成本结构，在AI时代如何重塑我们的决策路径。

After weeks of severe insomnia, I used AI to build an iOS app that exported HealthKit data and ran multivariate regression to find the root cause—late-night AI-assisted intense multitasking. This post explores how AI provided end-to-end execution support and why certain things still require human judgment.

在iOS上查询排版结果只需一行代码，Web上需要触发整个页面的重新布局。这不是因为浏览器工程师蠢，而是CSS在1994年做了一个声明式的架构选择。这个选择的天花板更高，但代价是中间状态不可查询。Facebook在2012年因为不理解这个trade-off付出了数亿美元的代价。SwiftUI和Jetpack Compose证明了声明式和可观测可以共存，关键在于分层。这个教训适用于所有系统设计：好的抽象让你选择在哪一层工作，坏的抽象把所有层粘在一起让你没得选。

Querying layout results takes one line of code on iOS, Android, Qt, and Flutter. On the web, it requires triggering a full-page reflow. This isn't because browser engineers are incompetent. CSS made a deliberate architectural choice in 1994 toward declarative layout, which has a higher ceiling but hides intermediate state. Facebook paid hundreds of millions of dollars in 2012 for not understanding this trade-off. SwiftUI and Jetpack Compose prove that declarative and observable can coexist through proper layering. The lesson applies to all system design: good abstractions let you choose which layer to work at; bad abstractions glue all layers together and leave you no choice.

LLM的默认输出是consensus：正确但平庸。Deep Research其实是Wide Research。我们找到了一种系统性方法，用个人认知上下文把LLM从consensus里强行扯出来。一年实验，有控制变量证据。

An LLM's default output is consensus: correct but mediocre. Deep Research is really Wide Research. We found a systematic way to pull LLMs out of consensus using personal cognitive context. One year of experimentation, with controlled evidence.

会用AI和用好AI之间差的是10倍。这个差距的根源在于工作方式，而非模型。本文通过一个完整的工作流例子和上中下三策的框架，解释为什么应该从ChatGPT切换到Cursor这类Agentic工具。

The gap between using AI and using AI well is 10x. That gap comes from how you work, not which model you use. This post walks through a complete workflow example and a Three Tiers framework to explain why you should switch from ChatGPT to agentic tools like Cursor.

用两分钟指挥AI给300篇文章添加SEO summary的实战案例，拆解五个关键决策：选对执行环境、先建测试再干活、让agent自己处理corner case、divide and conquer、结果导向的prompt写法。

A real-world case study of directing AI to add SEO summaries to 300 articles in two minutes, breaking down five key decisions: choosing the right execution environment, building tests before work, letting agents handle corner cases, divide and conquer, and outcome-oriented prompt writing.